1. Process Mining Control flow process discovery Fabrizio Maria Maggi (based on Process Mining book – Springer copyright 2011 and lecture material by Marlon Dumas, Wil van der Aalst and Ana Karla Alves de Medeiros http://www.processmining.org)http://www.processmining.org

2. Process Mining

3. Control-Flow Mining EventLog Discovery Techniques: Control-Flow Mining MinedModel 1.Start 2.Get Ready 3.Travel by Train 4.Beta Event Starts 5.Visit Brewery 6.Have Dinner 7.Go Home 8.Travel by Train 1.Start 2.Get Ready 3.Travel by Train 4.Beta Event Starts 5.Give a Talk 6.Visit Brewery 7.Have Dinner 8.Go Home 9.Travel by Train 1.Start 2.Get Ready 3.Travel by Car 4.Beta Event Starts 5.Give a Talk 6.Visit Brewery 7.Have Dinner 8.Go Home 9.Pay Parking 10.Travel by Car 1.Start 2.Get Ready 3.Travel by Car 4.Conference Starts 5.Join Reception 6.Have Dinner 7.Go Home 8.Pay Parking 9.Travel by Car 10.End Start Get Ready Travel by Train Train Travel by Car Car Conference Starts Give a Talk Join Reception Have Dinner Go Home Travel by Train Train Travel by Car Car PayParking End

4. Mining Common Constructs Sequence Splits Joins Loops Non-Free Choice Invisible Tasks Duplicate Tasks PayParking GetReady Travel by Train Train Travel by Car Car Defense Starts Give a Talk Ask Question Defense Ends Go Home Travel by Train Train Travel by Car Car Have Drinks

5. Mining Common Constructs Sequence Splits Joins Loops Non-Free Choice Invisible Tasks Duplicate Tasks PayParking GetReady Travel by Train Train Travel by Car Car Defense Starts Give a Talk Ask Question Defense Ends Go Home Travel by Train Train Travel by Car Car Have Drinks + noise in logs!

6. α-algorithm Basic Idea: Ordering relations Direct succession: x>y iff for some case x is directly followed by y. Causality: x  y iff x>y and not y>x. Parallel: x||y iff x>y and y>x Unrelated: x#y iff not x>y and not y>x. case 1 : task A case 2 : task A case 3 : task A case 3 : task B case 1 : task B case 1 : task C case 2 : task C case 4 : task A case 2 : task B... A>BA>CB>CB>DC>BC>DE>F ABABACACBDBDCDCDEFEFABABACACBDBDCDCDEFEF B||CC||BABCDACBDEF

7. Basic Idea: Example

9. Basic Idea: Footprints

10. Basic Idea: Patterns

11. α-algorithm

12. α-algorithm: Applicative Example

24. Limitations: short loops of length 1 b>b and not b>b implies bb (impossible!)

25. Limitations: short loops of length 1 Example “Short1”

26. Limitations: short loops of length 2 c>b and b>c implies c||b and b||c instead of cb and bc

27. Limitations: short loops of length 2 Example “Short2”

28. Limitations: non-free-choice nets Example “nonlocal”

29. Limitations: invisible tasks Example “invisible”

30. Mining Common Constructs Sequence Splits Joins Loops Non-Free Choice Invisible Tasks Duplicate Tasks PayParking GetReady Travel by Train Train Travel by Car Car Defense Starts Give a Talk Ask Question Defense Ends Go Home Travel by Train Train Travel by Car Car Have Drinks

31. Mining Common Constructs Sequence Splits Joins Loops Non-Free Choice Invisible Tasks Duplicate Tasks PayParking GetReady Travel by Train Train Travel by Car Car Defense Starts Give a Talk Ask Question Defense Ends Go Home Travel by Train Train Travel by Car Car Have Drinks + noise in logs!

32. Heuristic Miner

35. Example “heuristic”

36. Mining Common Constructs Sequence Splits Joins Loops Non-Free Choice Invisible Tasks Duplicate Tasks PayParking GetReady Travel by Train Train Travel by Car Car Defense Starts Give a Talk Ask Question Defense Ends Go Home Travel by Train Train Travel by Car Car Have Drinks

37. Mining Common Constructs Sequence Splits Joins Loops Non-Free Choice Invisible Tasks Duplicate Tasks PayParking GetReady Travel by Train Train Travel by Car Car Defense Starts Give a Talk Ask Question Defense Ends Go Home Travel by Train Train Travel by Car Car Have Drinks + noise in logs!

38. Genetic Miner

39. GPM – Fitness Measure Start Get Ready Travel by Train Train Travel by Car Car Conference Starts Give a Talk Visit Brewery Have Dinner Go Home Travel by Train Train Travel by Car Car PayParking End Guides the search!

40. Genetic Miner: Crossover

41. Genetic Miner: Mutation